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Allowing a robot to acquire 3D object models autonomously not only requires robust feature detection and learning methods but also mechanisms for guiding learning and assessing learning progress. In this paper we present probabilistic measures for observed detection success, predicted detection success and the completeness of learned models, where learning is incremental and online. This allows the...
The purpose of the study described here was to develop and feasibility test the Rutgers Ankle CP, aimed at ankle strengthening and improved control for children with cerebral palsy (CP). The system was an upgrade in hardware (new foot attachment, new robot controller) and software (new games and programming language) of the earlier Rutgers Ankle in order to permit training of children with CP. The...
Motor deficits in the growing population of stroke survivors continue to strain global healthcare capacities. The use of telerehabilitation to address this need has been discussed for over a decade without a clear consensus on development strategy or a clear market success. In this paper, the cyclic and iterative phases of the Planning, Execution, Assessment (PLEXAS) rehabilitation cycle are discussed,...
Nine children with cerebral palsy and nine adults with stroke were trained using 5 different upper extremity simulations using the NJIT-RAVR system for approximately nine to twelve hours over a three week period. Both groups made improvements in clinical measurements of upper extremity function and reaching kinematics. Patterns and magnitudes of improvement differ between the two groups. Responses...
An important problem in robotic manipulation is the ability to predict how objects behave under manipulative actions. This ability is necessary to allow planning of object manipulations. Physics simulators can be used to do this, but they model many kinds of object interaction poorly. An alternative is to learn a motion model for objects by interacting with them. In this paper we address the problem...
A robust robot perception system intended to enable object manipulation needs to be able to accurately identify objects and their pose at high speeds. Since objects vary considerably in surface properties, rigidity and articulation, no single detector or object estimation method has been shown to provide reliable detection across object types to date. This indicates the need for an architecture that...
Robotic training following stroke is an emerging rehabilitation technique to facilitate neuromuscular plasticity for regaining functional movements. Most existing training robots follow a defined task-based trajectory in assistive or resistive mode. Whilst this approach may be effective in certain cases it does not allow training of arm or leg in a natural way as the motion is precisely guided by...
This paper addresses the problem of learning and efficiently representing discriminative probabilistic models of object-specific grasp affordances particularly when the number of labeled grasps is extremely limited. The proposed method does not require an explicit 3D model but rather learns an implicit manifold on which it defines a probability distribution over grasp affordances. We obtain hypothetical...
This paper describes an approach for mobile robot localization using a visual word based place recognition approach. In our approach we exploit the benefits of a stereo camera system for place recognition. Visual words computed from SIFT features are combined with VIP (viewpoint invariant patches) features that use depth information from the stereo setup. The approach was evaluated under the ImageCLEF@ICPR...
In this paper, we propose a novel Adaboost template to recognize human upper body poses from disparity images for natural human robot interaction (HRI). First, the upper body poses of standing persons are classified into seven categories of views. For each category, a mean template, variance template, and percentage template are generated. Then, the template region is divided into positive and negative...
This paper will describe the NJIT-RAVR system, which combines adaptive robotics with complex VR simulations for the rehabilitation of upper extremity impairments and function in children with CP. The feasibility of this system is examined in the context of two pilot studies. The NJIT-RAVR system consists of the Haptic Master, a 6 degrees of freedom, admittance controlled robot and a suite of rehabilitation...
In most activities of daily living, related tasks are encountered over and over again. This regularity allows humans and robots to reuse existing solutions for known recurring tasks. We expect that reusing a set of standard solutions to solve similar tasks will facilitate the design and on-line adaptation of the control systems of robots operating in human environments. In this paper, we derive a...
In this paper we present an algorithm for multi-view object and pose recognition. In contrast to the existing work that focuses on modeling the object using the images only; we exploit the information on the image sequences and their relative 3D positions, because under many circumstances the movements between multi-views are accessible and can be controlled by the users. Thus we can calculate the...
Remote training is one of the rapid growing fields in computer applications. This paper will investigate the pivot technologies in remote training, specifically for remote control or operational training. By means of working with a test platform of robot telecontrol, the pivot technologies in remote operational training are identified, which could be propagate to the general training scenario, such...
Robust perception is a vital capability for robotic manipulation in unstructured scenes. In this context, full pose estimation of relevant objects in a scene is a critical step towards the introduction of robots into household environments. In this paper, we present an approach for building metric 3D models of objects using local descriptors from several images. Each model is optimized to fit a set...
This paper we propose a new rehabilitation training support system of upper limbs with the teaching/training function for personalized rehabilitation. The proposed teaching/training function enables the therapists to easily make not only training trajectories but also training programs to suit the individual needs of the patients. It is shown in this paper that three kinds of training programs requested...
Making a computer generate its own emotion is an important part of the affective computing, and this would have wide applications in human-computer interaction and artificial intelligence. In this paper, we will describe an emotion generation model for a multimodal virtual human. The relationship among the emotion, mood and personality are discussed firstly, and the PAD (pleasure-arousal-dominance)...
The present study is aimed at developing new rehabilitation protocols to be used in post-stroke robotic-aided therapy. In the recent past, it has been suggested that the use of robotic training forces that enhance error instead of reducing it, could stimulate new learning and feedback strategies at the base of an effective motor recovery. Starting from these findings, in this work two different robotic-aided...
Our goal is that the robot learns specific objects (not object category) from images. The major problem here is how to separate the target object from the background. We create a scene model from an image sequence. The scene model contains both the target object and background. We separate the target object from the background by matching the scene model and training images having different backgrounds...
The dispatching of robots into mission critical environments is becoming more and more commonplace as hardware evolves to a level of ruggedness demanded in these scenarios. Despite the advances in hardware platforms, novel control strategies to support effective human-robot interaction languish behind. Researchers at the Idaho National Laboratory (INL) and Washington University in St. Louis have been...
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